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Summary Table

NAME CATEGORY CITATION YEAR
Biobank Japan (BBJ) JENGER Biobanks_Cohorts NA NA
Biobank Japan (BBJ) Phewebjp Biobanks_Cohorts NA NA
Biobank Russia Biobanks_Cohorts Usoltsev D, Kolosov N, Rotar O, Loboda A, ...&, Artomov M. (2024) Complex trait susceptibilities and population diversity in a sample of 4,145 Russians Nat. Commun., 15 (1) 1-10. doi:10.1038/s41467-024-50304-1. PMID 39043636 2024
CARTaGENE PheWeb Biobanks_Cohorts NA NA
China Kadoorie Biobank (CKB) Biobanks_Cohorts NA NA
FinMetSeq Biobanks_Cohorts NA NA
FinnGen-UKBB meta-analysis Biobanks_Cohorts NA NA
Finngen 10 (December 18 2023) Biobanks_Cohorts Kurki MI, Karjalainen J, Palta P, Sipilä TP, ...&, Palotie A. (2023) FinnGen provides genetic insights from a well-phenotyped isolated population Nature, 613 (7944) 508-518. doi:10.1038/s41586-022-05473-8. PMID 36653562 2023
Finngen 11 (June 24 2024) Biobanks_Cohorts Kurki MI, Karjalainen J, Palta P, Sipilä TP, ...&, Palotie A. (2023) FinnGen provides genetic insights from a well-phenotyped isolated population Nature, 613 (7944) 508-518. doi:10.1038/s41586-022-05473-8. PMID 36653562 2023
Finngen 4 (November 30 2020) Biobanks_Cohorts Kurki MI, Karjalainen J, Palta P, Sipilä TP, ...&, Palotie A. (2023) FinnGen provides genetic insights from a well-phenotyped isolated population Nature, 613 (7944) 508-518. doi:10.1038/s41586-022-05473-8. PMID 36653562 2023
Finngen 5 (May 11 2021) Biobanks_Cohorts Kurki MI, Karjalainen J, Palta P, Sipilä TP, ...&, Palotie A. (2023) FinnGen provides genetic insights from a well-phenotyped isolated population Nature, 613 (7944) 508-518. doi:10.1038/s41586-022-05473-8. PMID 36653562 2023
Finngen 6 (January 24 2022) Biobanks_Cohorts Kurki MI, Karjalainen J, Palta P, Sipilä TP, ...&, Palotie A. (2023) FinnGen provides genetic insights from a well-phenotyped isolated population Nature, 613 (7944) 508-518. doi:10.1038/s41586-022-05473-8. PMID 36653562 2023
Finngen 7 (June 1 2022) Biobanks_Cohorts Kurki MI, Karjalainen J, Palta P, Sipilä TP, ...&, Palotie A. (2023) FinnGen provides genetic insights from a well-phenotyped isolated population Nature, 613 (7944) 508-518. doi:10.1038/s41586-022-05473-8. PMID 36653562 2023
Finngen 8 (Dec 1 2022) Biobanks_Cohorts Kurki MI, Karjalainen J, Palta P, Sipilä TP, ...&, Palotie A. (2023) FinnGen provides genetic insights from a well-phenotyped isolated population Nature, 613 (7944) 508-518. doi:10.1038/s41586-022-05473-8. PMID 36653562 2023
Finngen 9 (May 11 2023) Biobanks_Cohorts Kurki MI, Karjalainen J, Palta P, Sipilä TP, ...&, Palotie A. (2023) FinnGen provides genetic insights from a well-phenotyped isolated population Nature, 613 (7944) 508-518. doi:10.1038/s41586-022-05473-8. PMID 36653562 2023
Generation Scotland Biobanks_Cohorts NA NA
Global Biobank Biobanks_Cohorts Zhou W, Kanai M, Wu KH, Rasheed H, ...&, Neale BM. (2022) Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease Cell Genom., 2 (10) 100192. doi:10.1016/j.xgen.2022.100192. PMID 36777996 2022
KoGES Pheweb Biobanks_Cohorts NA NA
KoreanChip Biobanks_Cohorts NA NA
MGI 1 Biobanks_Cohorts NA NA
MGI 2 Biobanks_Cohorts NA NA
MGI BioUV Biobanks_Cohorts NA NA
Pan-UKB Biobanks_Cohorts NA NA
Taiwan BioBank Pheweb Biobanks_Cohorts NA NA
Tohoku Medical Megabank (TMM) Jmorp Biobanks_Cohorts NA NA
UKB Neale Biobanks_Cohorts NA NA
UKB TOPMed Biobanks_Cohorts NA NA
UKB exome Biobanks_Cohorts NA NA
UKB fastgwa-glmm Biobanks_Cohorts NA NA
UKB fastgwa Biobanks_Cohorts NA NA
UKB gene-based (Genebass) Biobanks_Cohorts Karczewski KJ, Solomonson M, Chao KR, Goodrich JK, ...&, Neale BM. (2022) Systematic single-variant and gene-based association testing of thousands of phenotypes in 394,841 UK Biobank exomes Cell Genom., 2 (9) 100168. doi:10.1016/j.xgen.2022.100168. PMID 36778668 2022
UKB saige Biobanks_Cohorts NA NA
DIAGRAM Consortiums NA NA
GIANT (Genetic Investigation of ANthropometric Traits) Consortiums NA NA
GLGC (Global Lipids Genetics Consortium) Consortiums NA NA
Megastroke Consortiums NA NA
PGC (Psychiatric Genomics Consortium) Consortiums NA NA
CNCR CTGLAB Institution NA NA
CNSGENOMICS Institution NA NA
Cardiovascular Disease Knowledge Portal Platform Costanzo MC, Roselli C, Brandes M, Duby M, ...&, Burtt NP. (2023) Cardiovascular disease knowledge portal: A community resource for cardiovascular disease research Circ. Genom. Precis. Med., 16 (6) e004181. doi:10.1161/CIRCGEN.123.004181. PMID 37814896 2023
GWAS catalog Platform Sollis E, Mosaku A, Abid A, Buniello A, ...&, Harris LW. (2023) The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource Nucleic Acids Res., 51 (D1) D977-D985. doi:10.1093/nar/gkac1010. PMID 36350656 2023
OpenGWAS Platform Elsworth, B., Lyon, M., Alexander, T., Liu, Y., Matthews, P., Hallett, J., ... & Hemani, G. (2020). The MRC IEU OpenGWAS data infrastructure. BioRxiv, 2020-08. NA

Biobanks_Cohorts

Biobank Japan (BBJ) JENGER

Biobank Japan (BBJ) Phewebjp

  • NAME : Biobank Japan (BBJ) Phewebjp
  • URL : https://pheweb.jp/
  • MAIN_ANCESTRY : EAS
  • RELATED_BIOBANK : biobank japan

Biobank Russia

  • NAME : Biobank Russia
  • URL : https://biobank.almazovcentre.ru/#
  • MAIN_ANCESTRY : EUR
  • TITLE : Complex trait susceptibilities and population diversity in a sample of 4,145 Russians
  • DOI : 10.1038/s41467-024-50304-1
  • ABSTRACT : AbstractThe population of Russia consists of more than 150 local ethnicities. The ethnic diversity and geographic origins, which extend from eastern Europe to Asia, make the population uniquely positioned to investigate the shared properties of inherited disease risks between European and Asian ancestries. We present the analysis of genetic and phenotypic data from a cohort of 4,145 individuals collected in three metro areas in western Russia. We show the presence of multiple admixed genetic ancestry clusters spanning from primarily European to Asian and high identity-by-descent sharing with the Finnish population. As a result, there was notable enrichment of Finnish-specific variants in Russia. We illustrate the utility of Russian-descent cohorts for discovery of novel population-specific genetic associations, as well as replication of previously identified associations that were thought to be population-specific in other cohorts. Finally, we provide access to a database of allele frequencies and GWAS results for 464 phenotypes.
  • COPYRIGHT : https://creativecommons.org/licenses/by/4.0
  • CITATION : Usoltsev D, Kolosov N, Rotar O, Loboda A, ...&, Artomov M. (2024) Complex trait susceptibilities and population diversity in a sample of 4,145 Russians Nat. Commun., 15 (1) 1-10. doi:10.1038/s41467-024-50304-1. PMID 39043636
  • JOURNAL_INFO : Nature communications ; Nat. Commun. ; 2024 ; 15 ; 1 ; 1-10
  • PUBMED_LINK : 39043636

CARTaGENE PheWeb

China Kadoorie Biobank (CKB)

FinMetSeq

FinnGen-UKBB meta-analysis

Finngen 10 (December 18 2023)

  • NAME : Finngen 10 (December 18 2023)
  • URL : https://r10.finngen.fi/
  • MAIN_ANCESTRY : EUR
  • RELATED_BIOBANK : Finngen
  • TITLE : FinnGen provides genetic insights from a well-phenotyped isolated population
  • DOI : 10.1038/s41586-022-05473-8
  • ABSTRACT : Population isolates such as those in Finland benefit genetic research because deleterious alleles are often concentrated on a small number of low-frequency variants (0.1% ≤ minor allele frequency < 5%). These variants survived the founding bottleneck rather than being distributed over a large number of ultrarare variants. Although this effect is well established in Mendelian genetics, its value in common disease genetics is less explored1,2. FinnGen aims to study the genome and national health register data of 500,000 Finnish individuals. Given the relatively high median age of participants (63 years) and the substantial fraction of hospital-based recruitment, FinnGen is enriched for disease end points. Here we analyse data from 224,737 participants from FinnGen and study 15 diseases that have previously been investigated in large genome-wide association studies (GWASs). We also include meta-analyses of biobank data from Estonia and the United Kingdom. We identified 30 new associations, primarily low-frequency variants, enriched in the Finnish population. A GWAS of 1,932 diseases also identified 2,733 genome-wide significant associations (893 phenome-wide significant (PWS), P < 2.6 × 10-11) at 2,496 (771 PWS) independent loci with 807 (247 PWS) end points. Among these, fine-mapping implicated 148 (73 PWS) coding variants associated with 83 (42 PWS) end points. Moreover, 91 (47 PWS) had an allele frequency of <5% in non-Finnish European individuals, of which 62 (32 PWS) were enriched by more than twofold in Finland. These findings demonstrate the power of bottlenecked populations to find entry points into the biology of common diseases through low-frequency, high impact variants.
  • COPYRIGHT : https://creativecommons.org/licenses/by/4.0
  • CITATION : Kurki MI, Karjalainen J, Palta P, Sipilä TP, ...&, Palotie A. (2023) FinnGen provides genetic insights from a well-phenotyped isolated population Nature, 613 (7944) 508-518. doi:10.1038/s41586-022-05473-8. PMID 36653562
  • JOURNAL_INFO : Nature ; Nature ; 2023 ; 613 ; 7944 ; 508-518
  • PUBMED_LINK : 36653562

Finngen 11 (June 24 2024)

  • NAME : Finngen 11 (June 24 2024)
  • URL : https://r11.finngen.fi/
  • MAIN_ANCESTRY : EUR
  • RELATED_BIOBANK : Finngen
  • TITLE : FinnGen provides genetic insights from a well-phenotyped isolated population
  • DOI : 10.1038/s41586-022-05473-8
  • ABSTRACT : Population isolates such as those in Finland benefit genetic research because deleterious alleles are often concentrated on a small number of low-frequency variants (0.1% ≤ minor allele frequency < 5%). These variants survived the founding bottleneck rather than being distributed over a large number of ultrarare variants. Although this effect is well established in Mendelian genetics, its value in common disease genetics is less explored1,2. FinnGen aims to study the genome and national health register data of 500,000 Finnish individuals. Given the relatively high median age of participants (63 years) and the substantial fraction of hospital-based recruitment, FinnGen is enriched for disease end points. Here we analyse data from 224,737 participants from FinnGen and study 15 diseases that have previously been investigated in large genome-wide association studies (GWASs). We also include meta-analyses of biobank data from Estonia and the United Kingdom. We identified 30 new associations, primarily low-frequency variants, enriched in the Finnish population. A GWAS of 1,932 diseases also identified 2,733 genome-wide significant associations (893 phenome-wide significant (PWS), P < 2.6 × 10-11) at 2,496 (771 PWS) independent loci with 807 (247 PWS) end points. Among these, fine-mapping implicated 148 (73 PWS) coding variants associated with 83 (42 PWS) end points. Moreover, 91 (47 PWS) had an allele frequency of <5% in non-Finnish European individuals, of which 62 (32 PWS) were enriched by more than twofold in Finland. These findings demonstrate the power of bottlenecked populations to find entry points into the biology of common diseases through low-frequency, high impact variants.
  • COPYRIGHT : https://creativecommons.org/licenses/by/4.0
  • CITATION : Kurki MI, Karjalainen J, Palta P, Sipilä TP, ...&, Palotie A. (2023) FinnGen provides genetic insights from a well-phenotyped isolated population Nature, 613 (7944) 508-518. doi:10.1038/s41586-022-05473-8. PMID 36653562
  • JOURNAL_INFO : Nature ; Nature ; 2023 ; 613 ; 7944 ; 508-518
  • PUBMED_LINK : 36653562

Finngen 4 (November 30 2020)

  • NAME : Finngen 4 (November 30 2020)
  • URL : https://r4.finngen.fi/about
  • MAIN_ANCESTRY : EUR
  • RELATED_BIOBANK : Finngen
  • TITLE : FinnGen provides genetic insights from a well-phenotyped isolated population
  • DOI : 10.1038/s41586-022-05473-8
  • ABSTRACT : Population isolates such as those in Finland benefit genetic research because deleterious alleles are often concentrated on a small number of low-frequency variants (0.1% ≤ minor allele frequency < 5%). These variants survived the founding bottleneck rather than being distributed over a large number of ultrarare variants. Although this effect is well established in Mendelian genetics, its value in common disease genetics is less explored1,2. FinnGen aims to study the genome and national health register data of 500,000 Finnish individuals. Given the relatively high median age of participants (63 years) and the substantial fraction of hospital-based recruitment, FinnGen is enriched for disease end points. Here we analyse data from 224,737 participants from FinnGen and study 15 diseases that have previously been investigated in large genome-wide association studies (GWASs). We also include meta-analyses of biobank data from Estonia and the United Kingdom. We identified 30 new associations, primarily low-frequency variants, enriched in the Finnish population. A GWAS of 1,932 diseases also identified 2,733 genome-wide significant associations (893 phenome-wide significant (PWS), P < 2.6 × 10-11) at 2,496 (771 PWS) independent loci with 807 (247 PWS) end points. Among these, fine-mapping implicated 148 (73 PWS) coding variants associated with 83 (42 PWS) end points. Moreover, 91 (47 PWS) had an allele frequency of <5% in non-Finnish European individuals, of which 62 (32 PWS) were enriched by more than twofold in Finland. These findings demonstrate the power of bottlenecked populations to find entry points into the biology of common diseases through low-frequency, high impact variants.
  • COPYRIGHT : https://creativecommons.org/licenses/by/4.0
  • CITATION : Kurki MI, Karjalainen J, Palta P, Sipilä TP, ...&, Palotie A. (2023) FinnGen provides genetic insights from a well-phenotyped isolated population Nature, 613 (7944) 508-518. doi:10.1038/s41586-022-05473-8. PMID 36653562
  • JOURNAL_INFO : Nature ; Nature ; 2023 ; 613 ; 7944 ; 508-518
  • PUBMED_LINK : 36653562

Finngen 5 (May 11 2021)

  • NAME : Finngen 5 (May 11 2021)
  • URL : https://r5.finngen.fi/about
  • MAIN_ANCESTRY : EUR
  • RELATED_BIOBANK : Finngen
  • TITLE : FinnGen provides genetic insights from a well-phenotyped isolated population
  • DOI : 10.1038/s41586-022-05473-8
  • ABSTRACT : Population isolates such as those in Finland benefit genetic research because deleterious alleles are often concentrated on a small number of low-frequency variants (0.1% ≤ minor allele frequency < 5%). These variants survived the founding bottleneck rather than being distributed over a large number of ultrarare variants. Although this effect is well established in Mendelian genetics, its value in common disease genetics is less explored1,2. FinnGen aims to study the genome and national health register data of 500,000 Finnish individuals. Given the relatively high median age of participants (63 years) and the substantial fraction of hospital-based recruitment, FinnGen is enriched for disease end points. Here we analyse data from 224,737 participants from FinnGen and study 15 diseases that have previously been investigated in large genome-wide association studies (GWASs). We also include meta-analyses of biobank data from Estonia and the United Kingdom. We identified 30 new associations, primarily low-frequency variants, enriched in the Finnish population. A GWAS of 1,932 diseases also identified 2,733 genome-wide significant associations (893 phenome-wide significant (PWS), P < 2.6 × 10-11) at 2,496 (771 PWS) independent loci with 807 (247 PWS) end points. Among these, fine-mapping implicated 148 (73 PWS) coding variants associated with 83 (42 PWS) end points. Moreover, 91 (47 PWS) had an allele frequency of <5% in non-Finnish European individuals, of which 62 (32 PWS) were enriched by more than twofold in Finland. These findings demonstrate the power of bottlenecked populations to find entry points into the biology of common diseases through low-frequency, high impact variants.
  • COPYRIGHT : https://creativecommons.org/licenses/by/4.0
  • CITATION : Kurki MI, Karjalainen J, Palta P, Sipilä TP, ...&, Palotie A. (2023) FinnGen provides genetic insights from a well-phenotyped isolated population Nature, 613 (7944) 508-518. doi:10.1038/s41586-022-05473-8. PMID 36653562
  • JOURNAL_INFO : Nature ; Nature ; 2023 ; 613 ; 7944 ; 508-518
  • PUBMED_LINK : 36653562

Finngen 6 (January 24 2022)

  • NAME : Finngen 6 (January 24 2022)
  • URL : https://r6.finngen.fi/about
  • MAIN_ANCESTRY : EUR
  • RELATED_BIOBANK : Finngen
  • TITLE : FinnGen provides genetic insights from a well-phenotyped isolated population
  • DOI : 10.1038/s41586-022-05473-8
  • ABSTRACT : Population isolates such as those in Finland benefit genetic research because deleterious alleles are often concentrated on a small number of low-frequency variants (0.1% ≤ minor allele frequency < 5%). These variants survived the founding bottleneck rather than being distributed over a large number of ultrarare variants. Although this effect is well established in Mendelian genetics, its value in common disease genetics is less explored1,2. FinnGen aims to study the genome and national health register data of 500,000 Finnish individuals. Given the relatively high median age of participants (63 years) and the substantial fraction of hospital-based recruitment, FinnGen is enriched for disease end points. Here we analyse data from 224,737 participants from FinnGen and study 15 diseases that have previously been investigated in large genome-wide association studies (GWASs). We also include meta-analyses of biobank data from Estonia and the United Kingdom. We identified 30 new associations, primarily low-frequency variants, enriched in the Finnish population. A GWAS of 1,932 diseases also identified 2,733 genome-wide significant associations (893 phenome-wide significant (PWS), P < 2.6 × 10-11) at 2,496 (771 PWS) independent loci with 807 (247 PWS) end points. Among these, fine-mapping implicated 148 (73 PWS) coding variants associated with 83 (42 PWS) end points. Moreover, 91 (47 PWS) had an allele frequency of <5% in non-Finnish European individuals, of which 62 (32 PWS) were enriched by more than twofold in Finland. These findings demonstrate the power of bottlenecked populations to find entry points into the biology of common diseases through low-frequency, high impact variants.
  • COPYRIGHT : https://creativecommons.org/licenses/by/4.0
  • CITATION : Kurki MI, Karjalainen J, Palta P, Sipilä TP, ...&, Palotie A. (2023) FinnGen provides genetic insights from a well-phenotyped isolated population Nature, 613 (7944) 508-518. doi:10.1038/s41586-022-05473-8. PMID 36653562
  • JOURNAL_INFO : Nature ; Nature ; 2023 ; 613 ; 7944 ; 508-518
  • PUBMED_LINK : 36653562

Finngen 7 (June 1 2022)

  • NAME : Finngen 7 (June 1 2022)
  • URL : https://r7.finngen.fi/about
  • MAIN_ANCESTRY : EUR
  • RELATED_BIOBANK : Finngen
  • TITLE : FinnGen provides genetic insights from a well-phenotyped isolated population
  • DOI : 10.1038/s41586-022-05473-8
  • ABSTRACT : Population isolates such as those in Finland benefit genetic research because deleterious alleles are often concentrated on a small number of low-frequency variants (0.1% ≤ minor allele frequency < 5%). These variants survived the founding bottleneck rather than being distributed over a large number of ultrarare variants. Although this effect is well established in Mendelian genetics, its value in common disease genetics is less explored1,2. FinnGen aims to study the genome and national health register data of 500,000 Finnish individuals. Given the relatively high median age of participants (63 years) and the substantial fraction of hospital-based recruitment, FinnGen is enriched for disease end points. Here we analyse data from 224,737 participants from FinnGen and study 15 diseases that have previously been investigated in large genome-wide association studies (GWASs). We also include meta-analyses of biobank data from Estonia and the United Kingdom. We identified 30 new associations, primarily low-frequency variants, enriched in the Finnish population. A GWAS of 1,932 diseases also identified 2,733 genome-wide significant associations (893 phenome-wide significant (PWS), P < 2.6 × 10-11) at 2,496 (771 PWS) independent loci with 807 (247 PWS) end points. Among these, fine-mapping implicated 148 (73 PWS) coding variants associated with 83 (42 PWS) end points. Moreover, 91 (47 PWS) had an allele frequency of <5% in non-Finnish European individuals, of which 62 (32 PWS) were enriched by more than twofold in Finland. These findings demonstrate the power of bottlenecked populations to find entry points into the biology of common diseases through low-frequency, high impact variants.
  • COPYRIGHT : https://creativecommons.org/licenses/by/4.0
  • CITATION : Kurki MI, Karjalainen J, Palta P, Sipilä TP, ...&, Palotie A. (2023) FinnGen provides genetic insights from a well-phenotyped isolated population Nature, 613 (7944) 508-518. doi:10.1038/s41586-022-05473-8. PMID 36653562
  • JOURNAL_INFO : Nature ; Nature ; 2023 ; 613 ; 7944 ; 508-518
  • PUBMED_LINK : 36653562

Finngen 8 (Dec 1 2022)

  • NAME : Finngen 8 (Dec 1 2022)
  • URL : https://r8.finngen.fi/about
  • MAIN_ANCESTRY : EUR
  • RELATED_BIOBANK : Finngen
  • TITLE : FinnGen provides genetic insights from a well-phenotyped isolated population
  • DOI : 10.1038/s41586-022-05473-8
  • ABSTRACT : Population isolates such as those in Finland benefit genetic research because deleterious alleles are often concentrated on a small number of low-frequency variants (0.1% ≤ minor allele frequency < 5%). These variants survived the founding bottleneck rather than being distributed over a large number of ultrarare variants. Although this effect is well established in Mendelian genetics, its value in common disease genetics is less explored1,2. FinnGen aims to study the genome and national health register data of 500,000 Finnish individuals. Given the relatively high median age of participants (63 years) and the substantial fraction of hospital-based recruitment, FinnGen is enriched for disease end points. Here we analyse data from 224,737 participants from FinnGen and study 15 diseases that have previously been investigated in large genome-wide association studies (GWASs). We also include meta-analyses of biobank data from Estonia and the United Kingdom. We identified 30 new associations, primarily low-frequency variants, enriched in the Finnish population. A GWAS of 1,932 diseases also identified 2,733 genome-wide significant associations (893 phenome-wide significant (PWS), P < 2.6 × 10-11) at 2,496 (771 PWS) independent loci with 807 (247 PWS) end points. Among these, fine-mapping implicated 148 (73 PWS) coding variants associated with 83 (42 PWS) end points. Moreover, 91 (47 PWS) had an allele frequency of <5% in non-Finnish European individuals, of which 62 (32 PWS) were enriched by more than twofold in Finland. These findings demonstrate the power of bottlenecked populations to find entry points into the biology of common diseases through low-frequency, high impact variants.
  • COPYRIGHT : https://creativecommons.org/licenses/by/4.0
  • CITATION : Kurki MI, Karjalainen J, Palta P, Sipilä TP, ...&, Palotie A. (2023) FinnGen provides genetic insights from a well-phenotyped isolated population Nature, 613 (7944) 508-518. doi:10.1038/s41586-022-05473-8. PMID 36653562
  • JOURNAL_INFO : Nature ; Nature ; 2023 ; 613 ; 7944 ; 508-518
  • PUBMED_LINK : 36653562

Finngen 9 (May 11 2023)

  • NAME : Finngen 9 (May 11 2023)
  • URL : https://r9.finngen.fi/about
  • MAIN_ANCESTRY : EUR
  • RELATED_BIOBANK : Finngen
  • TITLE : FinnGen provides genetic insights from a well-phenotyped isolated population
  • DOI : 10.1038/s41586-022-05473-8
  • ABSTRACT : Population isolates such as those in Finland benefit genetic research because deleterious alleles are often concentrated on a small number of low-frequency variants (0.1% ≤ minor allele frequency < 5%). These variants survived the founding bottleneck rather than being distributed over a large number of ultrarare variants. Although this effect is well established in Mendelian genetics, its value in common disease genetics is less explored1,2. FinnGen aims to study the genome and national health register data of 500,000 Finnish individuals. Given the relatively high median age of participants (63 years) and the substantial fraction of hospital-based recruitment, FinnGen is enriched for disease end points. Here we analyse data from 224,737 participants from FinnGen and study 15 diseases that have previously been investigated in large genome-wide association studies (GWASs). We also include meta-analyses of biobank data from Estonia and the United Kingdom. We identified 30 new associations, primarily low-frequency variants, enriched in the Finnish population. A GWAS of 1,932 diseases also identified 2,733 genome-wide significant associations (893 phenome-wide significant (PWS), P < 2.6 × 10-11) at 2,496 (771 PWS) independent loci with 807 (247 PWS) end points. Among these, fine-mapping implicated 148 (73 PWS) coding variants associated with 83 (42 PWS) end points. Moreover, 91 (47 PWS) had an allele frequency of <5% in non-Finnish European individuals, of which 62 (32 PWS) were enriched by more than twofold in Finland. These findings demonstrate the power of bottlenecked populations to find entry points into the biology of common diseases through low-frequency, high impact variants.
  • COPYRIGHT : https://creativecommons.org/licenses/by/4.0
  • CITATION : Kurki MI, Karjalainen J, Palta P, Sipilä TP, ...&, Palotie A. (2023) FinnGen provides genetic insights from a well-phenotyped isolated population Nature, 613 (7944) 508-518. doi:10.1038/s41586-022-05473-8. PMID 36653562
  • JOURNAL_INFO : Nature ; Nature ; 2023 ; 613 ; 7944 ; 508-518
  • PUBMED_LINK : 36653562

Generation Scotland

Global Biobank

  • NAME : Global Biobank
  • URL : http://results.globalbiobankmeta.org/
  • MAIN_ANCESTRY : ALL
  • TITLE : Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease
  • DOI : 10.1016/j.xgen.2022.100192
  • ABSTRACT : Biobanks facilitate genome-wide association studies (GWASs), which have mapped genomic loci across a range of human diseases and traits. However, most biobanks are primarily composed of individuals of European ancestry. We introduce the Global Biobank Meta-analysis Initiative (GBMI)-a collaborative network of 23 biobanks from 4 continents representing more than 2.2 million consented individuals with genetic data linked to electronic health records. GBMI meta-analyzes summary statistics from GWASs generated using harmonized genotypes and phenotypes from member biobanks for 14 exemplar diseases and endpoints. This strategy validates that GWASs conducted in diverse biobanks can be integrated despite heterogeneity in case definitions, recruitment strategies, and baseline characteristics. This collaborative effort improves GWAS power for diseases, benefits understudied diseases, and improves risk prediction while also enabling the nomination of disease genes and drug candidates by incorporating gene and protein expression data and providing insight into the underlying biology of human diseases and traits.
  • COPYRIGHT : http://creativecommons.org/licenses/by-nc-nd/4.0/
  • CITATION : Zhou W, Kanai M, Wu KH, Rasheed H, ...&, Neale BM. (2022) Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease Cell Genom., 2 (10) 100192. doi:10.1016/j.xgen.2022.100192. PMID 36777996
  • JOURNAL_INFO : Cell genomics ; Cell Genom. ; 2022 ; 2 ; 10 ; 100192
  • PUBMED_LINK : 36777996

KoGES Pheweb

KoreanChip

MGI 1

MGI 2

MGI BioUV

Pan-UKB

Taiwan BioBank Pheweb

Tohoku Medical Megabank (TMM) Jmorp

UKB Neale

UKB TOPMed

UKB exome

UKB fastgwa

UKB fastgwa-glmm

UKB gene-based (Genebass)

  • NAME : UKB gene-based (Genebass)
  • URL : https://genebass.org/
  • MAIN_ANCESTRY : EUR
  • RELATED_BIOBANK : UK Biobank
  • TITLE : Systematic single-variant and gene-based association testing of thousands of phenotypes in 394,841 UK Biobank exomes
  • DOI : 10.1016/j.xgen.2022.100168
  • ABSTRACT : Genome-wide association studies have successfully discovered thousands of common variants associated with human diseases and traits, but the landscape of rare variations in human disease has not been explored at scale. Exome-sequencing studies of population biobanks provide an opportunity to systematically evaluate the impact of rare coding variations across a wide range of phenotypes to discover genes and allelic series relevant to human health and disease. Here, we present results from systematic association analyses of 4,529 phenotypes using single-variant and gene tests of 394,841 individuals in the UK Biobank with exome-sequence data. We find that the discovery of genetic associations is tightly linked to frequency and is correlated with metrics of deleteriousness and natural selection. We highlight biological findings elucidated by these data and release the dataset as a public resource alongside the Genebass browser for rapidly exploring rare-variant association results.
  • COPYRIGHT : http://creativecommons.org/licenses/by/4.0/
  • CITATION : Karczewski KJ, Solomonson M, Chao KR, Goodrich JK, ...&, Neale BM. (2022) Systematic single-variant and gene-based association testing of thousands of phenotypes in 394,841 UK Biobank exomes Cell Genom., 2 (9) 100168. doi:10.1016/j.xgen.2022.100168. PMID 36778668
  • JOURNAL_INFO : Cell genomics ; Cell Genom. ; 2022 ; 2 ; 9 ; 100168
  • PUBMED_LINK : 36778668

UKB saige

Consortiums

DIAGRAM

GIANT (Genetic Investigation of ANthropometric Traits)

GLGC (Global Lipids Genetics Consortium)

Megastroke

PGC (Psychiatric Genomics Consortium)

Institution

CNCR CTGLAB

CNSGENOMICS

Platform

Cardiovascular Disease Knowledge Portal

  • NAME : Cardiovascular Disease Knowledge Portal
  • URL : https://cvd.hugeamp.org/
  • TITLE : Cardiovascular disease knowledge portal: A community resource for cardiovascular disease research
  • DOI : 10.1161/CIRCGEN.123.004181
  • CITATION : Costanzo MC, Roselli C, Brandes M, Duby M, ...&, Burtt NP. (2023) Cardiovascular disease knowledge portal: A community resource for cardiovascular disease research Circ. Genom. Precis. Med., 16 (6) e004181. doi:10.1161/CIRCGEN.123.004181. PMID 37814896
  • JOURNAL_INFO : Circulation. Genomic and precision medicine ; Circ. Genom. Precis. Med. ; 2023 ; 16 ; 6 ; e004181
  • PUBMED_LINK : 37814896

GWAS catalog

  • NAME : GWAS catalog
  • URL : https://www.ebi.ac.uk/gwas/
  • TITLE : The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource
  • DOI : 10.1093/nar/gkac1010
  • ABSTRACT : The NHGRI-EBI GWAS Catalog (www.ebi.ac.uk/gwas) is a FAIR knowledgebase providing detailed, structured, standardised and interoperable genome-wide association study (GWAS) data to >200 000 users per year from academic research, healthcare and industry. The Catalog contains variant-trait associations and supporting metadata for >45 000 published GWAS across >5000 human traits, and >40 000 full P-value summary statistics datasets. Content is curated from publications or acquired via author submission of prepublication summary statistics through a new submission portal and validation tool. GWAS data volume has vastly increased in recent years. We have updated our software to meet this scaling challenge and to enable rapid release of submitted summary statistics. The scope of the repository has expanded to include additional data types of high interest to the community, including sequencing-based GWAS, gene-based analyses and copy number variation analyses. Community outreach has increased the number of shared datasets from under-represented traits, e.g. cancer, and we continue to contribute to awareness of the lack of population diversity in GWAS. Interoperability of the Catalog has been enhanced through links to other resources including the Polygenic Score Catalog and the International Mouse Phenotyping Consortium, refinements to GWAS trait annotation, and the development of a standard format for GWAS data.
  • COPYRIGHT : https://creativecommons.org/licenses/by/4.0/
  • CITATION : Sollis E, Mosaku A, Abid A, Buniello A, ...&, Harris LW. (2023) The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource Nucleic Acids Res., 51 (D1) D977-D985. doi:10.1093/nar/gkac1010. PMID 36350656
  • JOURNAL_INFO : Nucleic acids research ; Nucleic Acids Res. ; 2023 ; 51 ; D1 ; D977-D985
  • PUBMED_LINK : 36350656

OpenGWAS

  • NAME : OpenGWAS
  • URL : https://gwas.mrcieu.ac.uk/
  • PREPRINT_DOI : 10.1101/2020.08.10.244293
  • SERVER : biorxiv
  • CITATION : Elsworth, B., Lyon, M., Alexander, T., Liu, Y., Matthews, P., Hallett, J., ... & Hemani, G. (2020). The MRC IEU OpenGWAS data infrastructure. BioRxiv, 2020-08.